The temperature vegetation dryness index (TVDI) has been commonly implemented to estimate regional soil moisture in arid and semi-arid regions. However, the parameterization of the dry edge in the TVDI model is performed with a constraint to define the maximum water stress conditions. Mismatch of the spatial scale between visible and thermal bands retrieved from remotely sensed data and terrain variations also affect the effectiveness of the TVDI. Therefore, this study proposed a new drought index named the condition vegetation drought index (CVDI) to monitor the temporal and spatial variations of soil moisture status by substituting the land surface temperature (LST) with the modified perpendicular drought index (MPDI). In situ soil moisture observations at crop and pasture sites in Victoria were used to validate the effectiveness of the CVDI. The results indicate that the dry and wet edges in the parameterization scheme of the CVDI formed a better-defined trapezoid shape than that of the TVDI. Compared with the MPDI and TVDI for soil moisture monitoring at crop sites, the CVDI exhibited a performance superior to the MPDI and TVDI in most days where the coefficients of determination (R²) achieved can reach to 0.67 on DOY023, 137, 274 and 0.71 on DOY 322 and reproduced more accurate spatial and seasonal variation of soil moisture. Moreover, the CVDI showed higher correlation with the Australian Water Resource Assessment Landscape (AWRA-L) soil moisture product on temporal scales. The R² can reach to 0.69 and the root mean square error (RMSE) is also much better than that of the MPDI and TVDI. Overall, it can be concluded that the CVDI appears to be a feasible method and can be successfully used in regional soil moisture monitoring.

Remote sensing inversion of vegetation-covered soil moisture is often affected by crop canopy, surface roughness, and other factors. In order to eliminate the scattering influence of vegetation effectively, this paper developed a modified vegetation backscattering model to retrieve vegetation-covered soil moisture based on multi-temporal RADARSAT-2 data and field measurements. This model combined the advantages of optical and radar methods by considering scattering contributions of underlying bare soil and vegetation canopy. Vegetation coverage was used to separate the scattering mechanism of the vegetation from bare soil component in a pixel. In addition, advanced integral equation method was presented to define the scattering of underlying bare soil. PROSAIL optical model was applied to calculate crown water content, which is an important variable associated with the scattering of vegetation canopy. Results demonstrated that the modified model on March 29, 2014 performed better in soil moisture retrieval than that at other growth stages with R2 of 0.806 and root-mean-square error of 0.043 m3·m−3, respectively. Soil moisture can be effectively retrieved by using the modified model in an agricultural region where the surface type is ranging from relatively sparse to full cover. Overall, the modified model provides an insight into extensive application of vegetation-covered soil moisture retrieval in agricultural regions.

X-band KOMPSAT-5 provides a good perspective for soil moisture retrieval at high-spatial resolution over arid and semi-arid areas. In this paper, an intercomparison of KOMPSAT-5 and C-band Sentinel-1 radar data in soil moisture retrieval was conducted over agricultural fields in Wimmera, Victoria, Australia. Optical images from Sentinel-2 were also used to calculate the scattering contribution of vegetation. This study employed a new semi-empirical vegetation scattering model with a linear association of soil moisture with observed backscatter coefficient and vegetation indices. The Combined Vegetation Index (CVI) was proposed and first used to parameterize vegetation water content. As a result, the vegetation scattering model was developed to monitor soil moisture based on remotely sensed data and ground measurements. Application of the algorithm over dryland wheat field sites demonstrated that the estimated satellite-based soil moisture contents have good linear relationships with the ground measurements. The correlation coefficients (R) are 0.862 and 0.616, and the root mean square errors (RMSEs) have the values of 0.020 cm³/cm³ and 0.032 cm³/cm³ at X- and C-bands, respectively. Furthermore, the validation results also indicated that X-band provided higher consistent accuracy for soil moisture inversion than C-band. These results showed significant promise in retrieving soil moisture using KOMPSAT-5 and Sentinel-1 remotely sensed data at high-spatial resolution over agricultural fields, with subsequent uses for crop growth and yield estimation.

As the fundamental regulator of energy exchange in the vegetation–soil–atmosphere circulation system, soil moisture is a key parameter for drought monitoring and is indispensable to the land surface hydrological processes. In order to overcome the constraints of the Perpendicular Drought Index, PDI (performs poorly over the fields with dense vegetation and hard to construct the soil line), and the Temperature Vegetation Drought Index, TVDI (requires similar atmospheric forcing and large enough dimension of mapping area), in soil moisture monitoring, a new drought index (Normalized Temperature Drought Index, NTDI) is proposed to explore the spatiotemporal changes of soil moisture by substituting red and near-infrared reflectances with vegetation index and normalized land surface temperature on the basis of the PDI framework. Victoria, Australia, was selected as the study area as it experiences many severe droughts and has been affected for more than ten years. Time series of satellite-based data were applied to evaluate the effectiveness and applicability of the NTDI at the regional scale. Results indicated that the expression of the soil line representing the water condition of the bare soil is easier to obtain in the new trapezoid framework and has good fits with the coefficients of determination (R²) of more than 0.8. Compared with PDI, TVDI and Modified PDI (MPDI) at the cropping sites, NTDI exhibits a relatively better performance in soil moisture monitoring for most days where the R² achieved can reach to more than 0.7 on DOY 242, 254 and 272. Meanwhile, spatial–temporal mappings of the four drought indices from satellite data were conducted, and the NTDI presented the slightly seasonal variation and effectively described the real spatial characteristics of regional drought. Overall, the NTDI seems to a viable approach and can provide insight into spatial and temporal soil moisture monitoring at different scales.

Soil moisture plays an essential role in the land-atmosphere interface. It has become necessary to develop quality large-scale soil moisture data from satellite observations for relevant applications in climate, hydrology, agriculture, etc. Specifically, microwave-based observations provide more consistent land surface records because they are unhindered by cloud conditions. The recent microwave radiometers onboard FY-3B, FY-3C and FY-3D satellites launched by China’s Meteorological Administration (CMA) extend the number of available microwave observations, covering late 2011 up until the present. These microwave observations have the potential to provide consistent global soil moisture records to date, filling the data gaps where soil moisture estimates are missing in the existing records. Along these lines, we studied the FY-3C to understand its added value due to its unique time of observation in a day (ascending: 22:15, descending: 10:15) absent from the existing satellite soil moisture records. Here, we used the triple collocation technique to optimize a benchmark retrieval model of land surface temperature (LST) tailored to the observation time of FY3C, by evaluating various soil moisture scenarios obtained with different bias-imposed LSTs from 2014 to 2016. The globally optimized LST was used as an input for the land parameter retrieval model (LPRM) algorithm to obtain optimized global soil moisture estimates. The obtained FY-3C soil moisture observations were evaluated with global in situ and reanalysis datasets relative to FY3B soil moisture products to understand their differences and consistencies. We found that the RMSEs of their anomalies were mostly concentrated between 0.05 and 0.15 m³ m⁻³, and correlation coefficients were between 0.4 and 0.7. The results showed that the FY-3C ascending data could better capture soil moisture dynamics than the FY-3B estimates. Both products were found to consistently complement the skill of each other over space and time globally. Finally, a linear combination approach that maximizes temporal correlations merged the ascending and descending soil moisture observations separately. The results indicated that superior soil moisture estimates are obtained from the combined product, which provides more reliable global soil moisture records both day and night. Therefore, this study aims to show that there is merit to the combined usage of the two FY-3 products, which will be extended to the FY-3D, to fill the gap in existing long-term global satellite soil moisture records.

Although surface soil moisture (SSM) is particularly important in crop yield prediction, irrigation scheduling optimization, and runoff generation mechanisms, accurate monitoring of time-series SSM is still challenging for agricultural and hydrological research. This study presented an improved approach integrating Sentinel-1 C-band SAR and MODIS optical data (2019–2020) to estimate surface soil moisture. To address vegetation effects, we developed a piecewise function using fractional vegetation coverage (FVC) to correct soil moisture and backscatter extrema and established the normalized difference enhanced vegetation index (NDEVI) to characterize backscatter-vegetation relationships across various land covers. Furthermore, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm identified anomalous surface changes, enabling segmentation of long-term series into invariant periods that satisfy the change detection method assumptions. Validation in the Shandian River Basin demonstrated significant improvement over traditional methods, achieving determination coefficients (R²) of 0.844 and root mean square errors (RMSE) of 0.030 m³/m³. The method effectively captured soil moisture dynamics from precipitation and irrigation events, providing reliable monitoring in heterogeneous landscapes. This integrated approach offers a robust technical framework for multi-source remote sensing of soil moisture in semi-arid areas, enhancing capability for agricultural water resource management.